Net transgressive sandstones form a significant component of many shallow-marine reservoirs, but their shale-poor character commonly masks complex facies architecture and stratigraphy associated with significant permeability variations that impact reservoir drainage patterns and ultimate recovery. In this article, the controls on net transgressive sandstone reservoir architecture are investigated through a detailed analysis of the Cretaceous Hosta Tongue of the Point Lookout Sandstone (informally termed Hosta sandstone in this article) outcrop in New Mexico. Mapping of facies architecture within a series of adjacent canyons has enabled a quantitative three-dimensional reconstruction of key stratigraphic surfaces and sand body distributions from an updip pinch-out to a downdip pinch-out of the net transgressive sandstone complex.

The Hosta sandstone contains a complex arrangement of wave- and tide-dominated facies associations arranged in an overall transgressive pattern. Tidal channel-fill sandstones, tidal sheet-form sandstones, and heterolithic tidal-flat and lagoonal deposits comprise the stratigraphy in the updip part of the system. These deposits pass abruptly downdip into wave-dominated shoreface sandstones. The facies composition indicates that the Hosta sandstone represents a wave-dominated barrier shoreline and a tide-dominated back-barrier lagoon. Facies associations are partitioned both vertically and laterally by a hierarchy of transgressive erosion (ravinement) surfaces cut by wave and tidal processes. Reconstructing the geomorphology and spatial organization of these surfaces is critical to understanding sand body distribution and facies architecture at high-resolution (intrareservoir) scale. The exceptional quality of the Hosta Sandstone outcrops has enabled (1) improved understanding of patterns and controls of facies architecture in net transgressive sandstone reservoirs, (2) construction of predictive templates of facies architecture in interwell volumes, and (3) quantification of geobody dimensions and spatial distribution patterns. In combination, these data provide appropriate qualitative and quantitative conditioning for reservoir models.